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Abstract

This study compares two methods for testing fertility trends and fertility stalls
using Demographic and Health Surveys data. The first method is based on linear regression
and uses the equivalence of period and cohort estimates with the same cumulative fertility
at age 40, the same number of births, and the same distribution of women by parity.
The second method is based on logistic regression. It assumes that the age pattern
of fertility is constant over short periods of time. Both methods were applied to
fertility trends in several African countries (Ghana, Kenya, Madagascar, Nigeria,
Rwanda, Senegal, Tanzania, and Zambia). The two methods were found to predict similar
values of cumulative fertility, to produce consistent slopes, to document fertility
trends the same way, and to characterize fertility stalls with similar statistical
evidence. They can also be used to refute apparent fertility stalls obtained when
comparing two point estimates from two successive surveys.

Keywords:

Introduction

Fertility decline from high values typical of natural fertility to near- or below-
replacement fertility is a quasi-universal phenomenon. Fertility decline started in
almost all countries in the world at some point in the 19th or 20th century with only a few exceptions, and the fertility transition finished (or was
almost finished) in a majority of countries by 2010. The United Nations Population
Division anticipates that fertility will continue to decline in the next 40 years
in the world as a whole. However, the fertility transition will not be completed by
2050 in countries that are currently classified as "high fertility, " most of them
in sub-Saharan Africa. In these countries, one expects a total fertility rate (TFR)
of 2.8 children per woman, whereas elsewhere fertility will be below replacement,
with TFR equaling 1.8 by 2050 [1]. The transition from high levels (i.e., five to eight children per woman) to low
levels of fertility (i.e., two or fewer children per woman) is usually continuous
and smooth and spans approximately 60 years, or two generations. Occasionally, this
transition can occur much faster (over 15 to 25 years) or slower (over a century or
more). A typical example of a smooth fertility transition in Europe is Sweden, where
the core of the transition occurred between 1870 and 1930 when the TFR dropped from
4.4 to 1.7 children per woman (corresponding cohorts are 1840 to 1900). Similar changes
were found throughout Europe, with the exception of France [2,3].

However, in some cases the transition is not continuous, and long periods of fertility
stalls have been documented. A typical case of fertility stall is that of Argentina,
where fertility dropped from 7.0 children per woman in 1895 to 3.2 children per woman
in 1947 and stayed at the same level for about 30 years before resuming its path in
recent years towards replacement fertility [4].

Sub-Saharan Africa is the continent that has started its fertility transition most
recently. Fertility levels are still high but tended to decline since the 1970s, and
declines occurred even earlier in urban areas of the most advanced countries. Most
documented cases of fertility decline in Africa seem to be smooth and continuous,
although it has been argued that fertility has stalled in a number of countries [5-16]. A comparison of these various studies shows that authors disagree whether or not
fertility has stalled in the same countries [13]. These discrepancies come from differences in the case definition of fertility stalls
and different statistical testing methods (or sometimes the lack of statistical testing
methods) [17,18].

The aim of this paper is to present and compare two simple methods of analyzing slopes
of fertility decline and testing fertility stalls. The first method is based on a
demographic reconstruction of synthetic cohorts, and the second is based on a direct
analysis of fertility rates. This work is a follow-up of earlier work on the same
topic [19-24].

Data and methods

Demographic and health surveys data

The Demographic and Health Surveys (DHS) are standard surveys based on representative
samples of national populations. Among other information, they provide maternity histories
for women aged 15 to 49 with details on the date of each birth and on the age of the
mother at time of each birth. This is enough information to compute person-years lived
and births by age and time period and therefore age-specific fertility rates by period
for the years preceding the survey. Note that since these maternity histories are
closed cohorts, these calculations can be done with basic tabulation and simple tools
such as a spreadsheet or basic computer programming. The only limitation of this type
of information is the truncation effect: one knows the fertility "x" years ago only
up to women aged "50 - x" years, because women who were interviewed were required
to be under 50 years of age. Therefore, we limited our analysis to fertility up to
age 40 and for the 10 years preceding each survey for which information is complete.
This truncation of retrospective surveys is inevitable; for example, a woman who gave
birth nine years ago at age 41 was not interviewed, as she was 50 at the time of the
survey. This effect is better explained in other documents [20,22]. DHS record all births that occurred prior to the survey, starting at age 12 years.
Cumulative fertility up to age 40 is noted as TFR(40) in this study and represents
the average number of children ever born per woman from age 12 to age 40 years, which
represents about 90% of the total fertility up to age 50 (the classic TFR).

Merging data from several surveys in the same country

If several surveys were available in a country, events and person-years were cumulated
in order to provide annual fertility rates for longer periods. The cumulating of several
surveys reduces fluctuations due to sample size and on average tends to compensate
for minor biases associated with sampling. When displayed in a figure, estimates of
cumulative fertility by age 40 grouped by two-year period tend to be quite regular
and reveal the major trends in fertility, whether increasing, decreasing, or remaining
steady. Of course, only formal statistical testing of slopes allows one to demonstrate
an increase in fertility, a fertility decline, or a fertility stall. This method has
been explained in more details in other documents [20,22,23].

All computations were done separately for urban and rural areas, since the trends
were often divergent at the beginning of the transition, with an earlier decline in
urban areas while fertility continued to rise or stagnated in rural areas.

Case definition of fertility stall

The criteria used for defining a fertility stall were similar to those proposed by
Gendell (1985): fertility decline must have started for some years, then the decline
must stop for a few years, and if the stall had come to an end, the fertility decline
must have resumed. In terms of slopes, this requires an initial period with a significant
negative slope, a second period with a net zero or positive slope, a significant change
in slopes between the first and second periods, and, when applicable, a third period
with a significant negative slope, with a significant change in slopes between the
second and third period (p < 0.05 using 2-tailed tests). We used only linear trends
for testing the changes in slopes, since these summarize well the changing slopes
and are easy to compute. Note that in any country, changes in total fertility rates
can almost always be approximated by linear trends over monotonic periods. The knots
defining monotonic periods were chosen visually after plotting the yearly cumulative
fertility on a graph and then fine-tuned by computing the intersection points of the
two regression lines. The slopes were calculated one by one, on each monotonic segment,
with the same linear trend.

Point estimates versus slopes

Our methods focused on slopes computed over periods for which annual fertility rates
were available. These methods are far more stable than simply comparing point estimates.
For example, in a DHS based on 6, 000 women, a TFR of 3.50 over the three years preceding
the survey could be given with a confidence interval of 3.15 to 3.85 (about 0.25 due
to sample size and 0.10 due to design effect). If two surveys are available five years
apart, it is almost impossible to test a trend from those two points, unless the difference
is very large (> 0.50). Even if the second survey indicates a TFR of 3.10, one cannot
rigorously conclude whether fertility declined or stayed constant. The testing of
slopes is very different, since it includes all points over the period covered, 10
years before each survey, totaling 15 years if two surveys are available. Furthermore,
merging datasets for computing slopes allows one to smooth out erratic values of point
estimates: these erratic fluctuations include fluctuations due to sample size and
design effect, so that a simple test is enough to prove the slope or the changing
of slopes.

The first method used for testing slopes and changes in slopes of fertility trends
follows a demographic approach and uses the property of period fertility rates. The
concept of TFR is abstract and refers to what is called a "synthetic cohort." In other
words, it computes what would be the cumulative fertility of a real cohort if it had
the same age-specific fertility rates as those observed over a given period. Here,
of course, one ignores mortality, as if all women survive up to age 40, as one would
do in a real cohort of women who already reached age 40. One could test the trends
in period cumulative fertility (period TFR) as if they were trends in cohort cumulative
fertility (i.e., equal to completed family size) with the same level and the same
number of births. For example, a period TFR(40) of 5.0 based on 1, 000 births is considered
to be equivalent to a cohort cumulative fertility of 5.0 among 200 women, who would
have had 1000 births by age 40. Testing trends in cohort fertility therefore requires
the distribution of completed family size by parity. As in the real world, when an
average completed family size is 5.0, the sample includes women with 0, 1, 2 ... 16+
children ever born, with an average of 5.0. Here the period TFR(40) was simply distributed
accordingly, by assuming that at the same level of cumulative fertility, the distribution
of women by parity was the same in a period and in a cohort (from 0 to 16+ children
ever born). This procedure allows one to obtain a direct measure of the slope and
its variance, based on individual women, as one would do in a cohort.

In practice, in Method 1 one proceeds the following way. First, one computes the cumulative
fertility, TFR(40), from age-specific fertility rates by single calendar year and
five-year age group. Then, one computes the corresponding number of women in the synthetic
cohort. These women are distributed by parity using a simple relationship linking
the proportion of women with (i) children to the completed family size. These relationships
were computed from cohort data using the same DHS, from parity 0 to parity 16+. Then,
the sample is analyzed as a cohort sample, and cumulative fertility is related to
time in a straightforward linear regression:

The model allows us to calculate cumulative fertility by year after linear fitting
and the precise fertility trend, to provide confidence intervals for slopes (positive,
negative, or zero), and to test for changing slopes using standard Student T-tests.
This method requires no hypothesis other than the equivalence between period and cohort,
which is the rationale for computing period fertility rates. A regression is also
calculated for each monotonic period.

The second approach focuses on age-specific fertility rates and is based on the fact
that women are likely to have only one or no delivery over a period of one year, depending
on age and period. Therefore, the method chosen is a linear-logistic model, or logistic
regression, where the dependent variable is 1 for a birth and 0 for no birth, and
the weights are proportionate to the exact person-years lived over the period. The
age pattern of fertility is complex and not easy to parameterize, so age groups (12-14,
15-19, 20-24, 25-29, 30-34, and 35-39) are introduced as dummy variables, with the
25-29 age group taken as the reference category because it has the largest number
of births and is therefore the most stable. The model is:

where i is the age group and Xi is the dummy variables associated with each age group, from 1 = 12-14, 2 = 15-19,
etc. and 6 = 35-39, with the fourth group (ages 25-29) omitted as reference category.

This model allows one to compute age-specific fertility rates by period, to recalculate
the cumulative fertility by age 40, and to estimate the trends. As in the first model,
it provides a confidence interval for the slopes and allows simple testing for fertility
stalls. This method requires only two basic hypotheses: homogeneity in risk of bearing
a child over a short period of time and a constant age pattern of fertility over short
periods of time, both of which appear realistic.

Results

Detailed example: Kenya, whole country

Both fitting methods were first applied to the case of Kenya, a country for which
most authors agree that there was a fertility stall in the recent past. As expected,
formal calculations demonstrate the fertility stall for the 1994 to 2002 period [see
Table 1]. The demographic approach based on linear regression indicates no change of cumulative
fertility by age 40 over the period 1994 to 2002, whereas fertility was declining
fast before (from 7.42 to 4.66 from 1980 to 1994) and after (from 4.90 to 3.93 from
2002 to 2008). Both changes in slope, before and after 1994 to 2002, were highly significant
(p < 10-6 in both cases). Results based on the logistic regression (the statistical approach)
confirm these findings with the same predicted values of cumulative fertility over
the same periods, same trends, consistent slopes, and similar levels of significance.

Testing for fertility stalls in African countries

The same procedure was repeated for a number of situations where fertility stalls
have been documented or suspected. Urban and rural areas were analyzed separately,
because they were found to have divergent fertility trends [22]. Fertility stalls were found in the following: Ghana, urban areas in 1998-2008; Kenya,
urban and rural areas in 1994-2002; Madagascar, urban areas in 1988-1994 and rural
areas in 1988-1998; Nigeria, rural areas in 1988-1998; Rwanda, urban areas in 1989-1997
and rural areas in 1997-2008; Senegal, urban areas in 1995-2002; Tanzania, rural areas
in 1996-2008; Zambia, rural areas in 1982-1997. In all cases, the fertility stall
was well demonstrated by statistical testing, and the changes in slope from fertility
decline to stall (and from stall to decline, when applicable) were highly significant
in almost all cases but one, with p < 10-3 [see Table 2]. The magnitude of the statistical evidence was comparable using either the linear
regression or the logistic regression. The second approach provided a higher level
of statistical significance in a majority of cases, although not in all cases.

Table 2. Comparison of two methods for testing fertility stalls in African countries

Checking for undocumented stalls

A number of fertility stalls were proposed by some authors but refuted by others [13]. These stalls were tested for countries as a whole, ignoring the urban/rural divide,
as is usually done by other analysts. For our testing, the baseline date was chosen
two years prior to the time at which the onset of the stall was proposed, since published
estimates of TFR calculated over three years apply to the fertility level 1.5 years
before the survey, on average. Among the seven countries investigated, none exhibited
a significant stall during the proposed period when using the complete yearly data
sets [See Table 3.] In Benin, Cameroon, and Mozambique, there were no significant changes in the slope
of fertility declines since the previous surveys (2001 in Benin, 1998 in Cameroon,
and 1997 in Mozambique), although the difference in slope was borderline when using
the logistic regression in Mozambique. In Ethiopia and Uganda, the fertility decline
accelerated during the second period (assumed to be a fertility stall), after 1999
in Ethiopia and after 1993 in Uganda. In Côte d'Ivoire and Zimbabwe, the speed of
fertility decline was reduced significantly during the second period (1997-2005 and
1999-2006, respectively), but remained negative and far from a fertility stall defined
by a slope equal to or above zero. In conclusion, none of these hypothesized stalls
was found to be statistically significant, whether using a linear regression model
or a logistic regression model.

Discussion

In this exercise, we showed that two different methods led to basically the same conclusions
with respect to fertility stalls and to equivalent slopes and confidence intervals.
This is reassuring, since cumulative period fertility can be considered as a synthetic
cohort or as the sum of age-specific fertility rates. This finding also indicates
that basic assumptions underlying both methods are likely to be fulfilled, namely
the equivalence of period and cohort and the constant age pattern of fertility during
the process of fertility changes. What the preferable method of testing is can be
discussed endlessly. The logistic regression method tends to provide lower p-values,
which suggests that it is more sensitive. However, the linear regression method might
be more specific and seems to provide a higher p-value when the case is borderline,
therefore ignoring false positives (as seen for Mozambique over the period 1995 to
2003). Therefore, it may be more realistic. In any case, both methods led to the same
conclusions in most instances.

Data quality is usually not an issue in DHS. Completed family size (or TFR) values
tend to be very consistent for the same cohorts (or periods) in the same country,
and comparisons with more precise data from demographic surveillance systems did not
reveal errors that could not be explained by random fluctuations due to sample size
and to cluster sampling [25]. Minor errors often found in DHS, such as the dating of birth around five years before
the survey, are unlikely to affect levels or trends over longer periods of time; they
will simply increase the variations in yearly estimates (i.e., data errors) and therefore
reduce the power of statistical tests.

Other authors have used Poisson regression, or negative-binomial regression, to do
similar testing of slopes. There is no obvious comparative advantage to using Poisson
or negative-binomial regressions when calculating yearly fertility rates, since most
women will have only one delivery at most over a 12-month period (the case would be
different when using five-year periods or longer). Therefore, a simple outcome of
0/1 as used in the logistic regression method seems to be more appropriate. We did
some basic comparisons of Poisson regression versus logistic regression and found
similar results in the case of Kenya displayed in Table 1. Further testing could be done to investigate whether Poisson or negative-binomial
regressions have any comparative advantage in this type of situation.

Other options are available for further testing fertility trends. For instance, one
could use an age pattern of fertility in each situation in order to make the testing
more precise. However, this is likely to be difficult. A Coale-Trussell function could
be tried, but is likely to miss premarital fertility, which accounts for 20% to 40%
of total fertility in some southern African countries [26]. Simpler functions such as polynomials could be tried, but probably with little advantage
when compared with the straightforward empirical pattern presented by dummy variables
associated with each age group.

Sophisticated statistical models have been developed in the past 20 years for testing
changing slopes of a response variable in a variety of situations. Some of these models,
such as "switching regression" or "change point regression" could also be tried to
estimate fertility trends from DHS [27-29].

Using retrospective data leads necessarily to some minor mortality biases, compared
with full-scale vital registration or prospective data. However, these biases are
likely to be small, since mortality between age 12 and 40 is usually very low. In
Africa, the high prevalence of HIV/AIDS and high mortality among young women could
lead to larger biases. This would lead to overestimating fertility levels in recent
years, since the fertility of HIV-infected women tends to be lower than that of others.
In theory, this could produce some apparent fertility stalls in retrospective surveys,
but in our studies we did not find any obvious correlation with HIV prevalence nor
with HIV mortality. This point could be further investigated when more data become
available.

Fertility stalls appear uncommon in African countries. Of the 31 countries investigated
in our earlier studies, only eight exhibited some kind of fertility stall, of which
five were restricted to either urban areas (Ghana and Senegal) or to rural areas (Nigeria,
Tanzania, Zambia), while the other three cases affected both urban and rural areas
(Kenya, Madagascar, and Rwanda). Most of these stalls were of short duration (< 10
years) or had been occurring for less than 10 years before the last survey. These
stalls of short duration do not compare with formal stalls such as that of Argentina,
which lasted for about 30 years (over an entire generation) at a much lower level
of TFR (about three children per women). Fertility stalls in Africa appear so far
to be minor accidents in the course of the fertility transition. However, if they
last longer, they could have serious consequences for long-term demographic dynamics,
especially when they occur at relatively high levels of fertility. Furthermore, African
countries are still in the middle of the fertility transition, and anything could
happen in the future. A recent study in the Pacific Islands showed that new forms
of fertility stalls or of fertility reversals could happen as a result of deliberate
reproductive strategies of couples. Because couples might have an economic advantage
to produce children who will be sent later in migration and who could remit money
to the family, they may choose to have more children [30].

Some of the fertility stalls proposed by other authors appeared undocumented in our
analysis. This is due to the differences in case definition and in statistical testing.
Using only two successive surveys with wide confidence intervals and point estimates
based on a period of three years before a survey could be misleading when compared
with a detailed analysis of fertility trends using all data available based on longer
periods of 10 years or more. The case is even more delicate when comparisons are made
on smaller sample sizes or when stalls are studied at the regional level or according
to socioeconomic characteristics.

More research could be conducted on the rationale for these well documented stalls.
In an earlier study, we showed that country situations were highly diverse, and one
could use a variety of factors to explain them without any consistent pattern [23].

More research could also be conducted on the provision of family planning services,
both in terms of quantity and in quality. Some authors have suggested that reduced
financing for family planning services could explain the fertility stalls [31]. This could be further analyzed, case by case, while separating urban and rural areas
whenever this is feasible.

Competing interests

The author declares that they have no competing interests.

Acknowledgements

The author thanks the measure-DHS project for providing free access to individual
data and for their support at an earlier stage of the research, and Institut Pasteur
and IRD for institutional support.

Pantelides EA: A century and a quarter of fertility change in Argentina: 1869 to the present. In The Fertility Transition in Latin America. Edited by Guzmán JM, Singh S, Rodríguez G, Pantelides EA. Oxford, Clarendon Press; 1996:345-358.